Locking Down MoT risks: The Power of Location & Operator Data

Claims, Coordinates & Corporate Checks: Insurers’ New Weapon Against MoT Fraud

Our new licensed MoT garages dataset isn’t just a list of garages—it’s an intelligence opportunity. By combining MoT test-centre data (location + operator) with our other datasets public like ratepayer names and Companies House, we’re enabling you to build a fraud-busting engine that learns and adapts as new claims stream in.

Spotting Suspicious Patterns at First Notice of Loss

As soon as a customer reports a vehicle claim, you can cross-reference:

• Test location against their claimed “usual garage.” Are they suddenly citing a previously unused MoT centre?
• Operator ID against your internal “risk tiers.” Have you seen that operator tied to suspicious claims before?
• Claims frequency per postcode. A spike of rear-ender claims all pointing to the same small cluster of garages is a red flag.

This rapid, automated check helps you catch outliers from day one—before payouts are approved.

Supercharging Fraud Models with Linked Corporate Data

It’s not enough to know “where” and “who” tested the car. You can go deeper by linking:

• Ratepayer Names (land registry, council tax)
• Companies House Records (directors, shareholder structures)
• Commercial Owner (Who holds the title to the location)

Why it matters:

• If an operator’s registered address is actually a private residence , you may downgrade its trust score.
• Companies House filings reveal when a garage changed ownership or directors—often a tell-tale sign that a problem operator has rebranded to escape poor compliance history.
• If the commercial owner of a location is linked to other suspicious activity then this may indicate that further investigation is required.

Every new claim automatically refreshes these linkages, keeping you risk scores razor-sharp.

Building Long-Term Operator & Location Profiles

Rather than isolated checks, you can create continuous dossiers on every MoT centre:

• Claim-linked pass/fail rates: Does this garage’s failure rate deviate from national or regional norms as seen in your claims?
• Retest-after-claim ratios: Are certain operators issuing a flurry of minor-defect re-tests following “minor” collisions?
• Corporate lifecycle tracking: You can flag when Companies House filings show common officers across multiple garage registrations—an indication of potential “shell” sites.

These profiles evolve with each claim, so your fraud models can learn which operator-location combos pose the greatest risk over time.

Automated Alerts & Case Prioritisation

With dynamic profiles in place, you can deploy rules and machine-learning triggers:

• Threshold breaches: If an operator’s “claims-linked pass rate” exceeds expected bounds for its postcode cluster, you can auto-escalate for manual review.
• Network-pattern alerts: Graph-analysis detects clusters of garages sharing directors, addresses or phone numbers—often a hallmark of organised fraud rings.
• Probationary tagging: Newly formed garages start with neutral risk, but even a small run of questionable claims can bump them into a “probation” category, prompting extra scrutiny.

This orchestration ensures your fraud investigators focus their time where it really matters.

Empowering Investigators & Business Users

Your fraud-analytics dashboard could expose all these layers at a glance:

• Interactive maps showing “hotspots” of suspicious MoT claims by operator.
• Drill-down reports on each garage’s corporate history, failure patterns, and claims associations.
• Time-series views to see how risk profiles rise or fall with new filings and claims.

Underwriters, investigators and compliance teams all tap the same data—no more siloed spreadsheets or ad-hoc lookups.

Delivering Better Outcomes for All

By weaving together MoT location/operator data, claims history, ratepayer registries and Companies House intelligence:

• Fraud losses shrink, as bogus certificates and staged claims are unmasked early.
• Payout cycles accelerate for legitimate customers, thanks to automated checks that clear low-risk claims instantly.
• Reputation gains, as insurers demonstrate sophisticated, fair-minded fraud detection and keep premiums as low as possible.

Conclusion

In today’s data-rich world, a claim is more than a request for money—it’s a signal. When you link MoT test-centre coordinates and operator IDs with your claims data, ratepayer registries and Companies House filings, you can turn every new claim into an opportunity to sharpen your fraud radar. The result? Faster approvals, targeted investigations, and a network of risk-profiles that keeps on learning—claim by claim.

Contact us now to find out how the Licensed MoT garages data can work for you!